Driver distraction, indecision, and high speed are the major causes of road vehicle accidents.
Modern road vehicles are equipped with numerous electronic controls which exchange information over an on-board BUS type data network known as a CAN (Car Area Network), which is supplied with the main dynamic data of the vehicle (i.e. longitudinal speed, individual wheel rotation speed, and longitudinal and lateral acceleration) and the commands imparted by the driver (i.e. steering angle, brake pressure, throttle position, engaged gear). An increasing number of vehicles are also equipped with a GPS receiver, which provides a fairly accurate, real-time georeference location of the vehicle.
Various methods of determining road vehicle driver behaviour have been proposed based on statistical algorithms, which determine anomalous driving behaviour solely on the basis of signals supplied by the above data networks on the vehicle. The driver is often alerted to these anomalies by acoustic and/or visual warnings to correct a potentially dangerous driving mode. The symptoms of anomalous driving behaviour are related to the commands imparted by the driver to the vehicle, and to the kinematic response of the vehicle to them (e.g. anomalous speed and/or acceleration), but this information can very easily be confused with anomalous traffic situations, and as such is of limited effectiveness and scope.
Other methods have also been proposed, which employ the instantaneous position of the vehicle on a georeference map stored in a database. A georeference map, however, poses several problems by having to be constantly updated, and by inevitably involving errors which may even seriously affect driver behaviour analysis.
Patent Application WO2008127465A1 describes a real-time, dangerous-driving prediction method which processes dynamic vehicle parameters, physiological driver data, and driver behaviour characteristics using an automatic-learning algorithm. More specifically, the method is based on algorithms which classify dangerous driving situations, such as a sharp bend, sudden acceleration/deceleration, erratic steering, etc., but fails to identify dangerous situations not taken into account at the algorithm learning stage. Moreover, this method, too, is not fully dependable in all driving situations, and may therefore mistake normal for dangerous behaviour, and vice versa.
What the state of the art does not take sufficiently into account is the extent to which anomalous-driving indicators depend on the motoring context. That is, no known method provides for a georeference (i.e. spatial location) of the dynamic vehicle signals or driver control signals used to determine driver behaviour.